Informatics and Computing
INF 626 - Applied Bayesian Modeling
Description: Bayesian statistical methods for analyzing data, with emphasis on ecological and biological data. Includes Bayes rule, basic Bayesian formulation (priors, posteriors, likelihoods), single- and multiple-parameter models, hierarchical models, generalized linear models, multivariate models, mixture models, models for missing data, merging statistical and process models, overview of spatial and temporal processes, and introduction to computation methods. Letter grade only.
Units:
3
Sections offered:
Fall 2024
Requirement Designation:
Prerequisite: Graduate Status